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207      
208        <span class="font-weight-bold">Elisabeth</span> 
209        Kirsten
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246              Bochum, Germany
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255<p><strong><a href="https://scholar.google.com/citations?user=mzjefFkAAAAJ" rel="external nofollow noopener" target="_blank"><code class="language-plaintext highlighter-rouge">Google Scholar</code></a></strong>   
256<strong><a href="https://github.com/elisakirsten" rel="external nofollow noopener" target="_blank"><code class="language-plaintext highlighter-rouge">GitHub</code></a></strong>   
257<strong><a href="https://linkedin.com/in/elisabeth-kirsten" rel="external nofollow noopener" target="_blank"><code class="language-plaintext highlighter-rouge">LinkedIn</code></a></strong>  </p>
258
259<p>👋 <strong>Short bio.</strong> I am a PhD student at <a href="https://informatik.rub.de/" rel="external nofollow noopener" target="_blank">Ruhr University Bochum</a> and <a href="https://rc-trust.ai/" rel="external nofollow noopener" target="_blank">Research Center for Trustworthy Data Science and Security</a>.
260I am coadvised by Dr. Muhammad Bilal Zafar and Dr. Nicole Krämer.</p>
261
262<p>My research focuses on human-centric and responsible Large Language Models. 
263I am passionate about societal impacts of AI systems as they scale across capabilities, domains, and human populations. 
264I am dedicated to computer science, ethics, and sociology, addressing a range of issues such as AI alignment, safety, and fairness from a multidisciplinary perspective. 
265I did my MSc at Technische Universität Darmstadt, and interned at EPFL and MPI-SP.</p>
266
267<p>📩 <strong>You can reach me at</strong>  <code style="font-size:0.75em;">first.last[at]rub.de</code></p>
268</div>
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284            <th scope="row" style="width: 20%">Oct 02, 2026</th>
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287                I will be presenting our paper <a href="https://arxiv.org/pdf/2609.04835" rel="external nofollow noopener" target="_blank">“On Epistemic Diversity in Large Language Models”</a> at COLM 2026 🌉. Come say hi if you are around!
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294            <th scope="row" style="width: 20%">Jul 28, 2026</th>
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297                I gave a <strong>tutorial</strong> on Multi-Agent Systems at the AutoML Summer School at TU Dortmund. Also seized this opportunity to launch a new <a href="/talks">Talks</a> page.
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304            <th scope="row" style="width: 20%">Jul 02, 2026</th>
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307                Our paper “On Epistemic Diversity in Large Language Models” got accepted at the <a href="https://sites.google.com/view/philmlicml26/home" rel="external nofollow noopener" target="_blank">“Philosophy Meets Machine Learning” Workshop</a> at ICML 2026. The camera-ready version is coming soon!
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314            <th scope="row" style="width: 20%">Jul 02, 2026</th>
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317                I am giving an <strong>Oral</strong> presentation at this year’s <a href="https://www2026.thewebconf.org/" rel="external nofollow noopener" target="_blank">Web Conference</a> on our paper <a href="https://dl.acm.org/doi/10.1145/3774904.3792671?__cf_chl_f_tk=WHtJm8QGtM.T_DrgskXaew4t27SR2Mu8XmpAJ4ioYFs-1783017299-1.0.1.1-q9Lk4zpNufaFy9qaz_x6m
317n7Ax6EMBD.6MpL4kUL8Ftk" rel="external nofollow noopener" target="_blank">“The Algorithmic Self-Portait: Deconstructing Memory in ChatGPT”</a>.
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324            <th scope="row" style="width: 20%">Jun 14, 2026</th>
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327                I was selected to participate in the <a href="http://mlss.cc/" rel="external nofollow noopener" target="_blank">Machine Learning Summer School</a> at Columbia University. I will be in NYC for two weeks before heading to San Diego for ACL. 🗽
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368    <div class="title">On Epistemic Diversity in Large Language Models</div>
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374      Elisabeth
375            Kirsten, Nicole C.
376            Krämer, and Muhammad Bilal
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393      <em>In COLM 2026</em>,  Oct 2026
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444        <p>Large language models (LLMs) are increasingly used not only to retrieve information, but to answer questions, explain, teach, and support inquiry. In such settings, evaluation cannot be exhausted by accuracy or alignment alone. A system may give a correct answer while still narrowing users’ access %to knowledge. to alternative valid answers, explanations, or reasoning routes. Drawing on the broader notion of epistemic diversity in philosophy and social epistemology, we formalize it in the context of LLMs as the range of valid answers, explanations, and reasoning routes that an LLM exposes to users. We argue that epistemic diversity is a useful evaluation dimension for settings where LLMs are used to support knowledge-intensive tasks. We propose a preliminary framework for conceptualizing and measuring epistemic diversity in LLMs, and operationalize it in two domains. We find that frontier LLMs often exhibit epistemic narrowness, repeatedly collapsing large valid answer spaces onto small canonical subsets. These findings suggest that LLM evaluation should move beyond accuracy-oriented paradigms and treat epistemic diversity as an important dimension of model capability.</p>
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472    <div class="title">Characterizing Web Search in The Age of Generative AI</div>
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478      Elisabeth
479            Kirsten, Jost Große
480            Perdekamp, Qinyuan
481            Wu, and
482        <span class="more-authors" title="click to view 3 more authors" onclick="
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510      <em>In Findings of the Association for Computational Linguistics: ACL 2026</em>,  Jul 2026
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561        <p>The advent of LLMs has given rise to generative search, a new search paradigm in which LLMs retrieve information from the web related to a query and synthesize it into a single, coherent response. This paradigm differs fundamentally from traditional web search, where results are returned as a ranked list of independent web pages. In this paper, we ask: Along what dimensions does generative search differ from traditional search?We conduct a systematic comparison between Google organic search and five generative search systems from three providers: Google, OpenAI, and Perplexity. Our analysis reveals substantial variation among engines in their reliance on internal v.s. external knowledge, source diversity, and stability. While generative systems often achieve topical coverage comparable to traditional search, they do so using markedly different retrieval footprints and synthesis strategies. We further show that the outputs of generative search can vary across time and executions, raising new challenges for robustness. Our findings demonstrate that generative search introduces new dimensions that are not captured by existing evaluation paradigms, motivating the development of evaluations that explicitly account for retrieval behavior, synthesis, and stability in generative search systems.</p>
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589    <div class="title">The algorithmic self-portrait: Deconstructing memory in ChatGPT</div>
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596            Dash, Soumi
597            Das, Elisabeth
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599        <span class="more-authors" title="click to view 6 more authors" onclick="
600              var element = $(this);
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602              var more_authors_text = element.text() == '6 more authors' ? 'Qinyuan Wu, Sai Keerthana Karnam, Krishna P Gummadi, Thorsten Holz, Muhammad Bilal Zafar, Savvas Zannettou' : '6 more authors';
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627      <em>In Proceedings of the ACM Web Conference 2026</em>,  Jul 2026
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652        <a href="https://github.com/SoumiDas/Memories_WWW_2026/" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a>
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678        <p>To enable personalized and context-aware interactions, conversational AI systems have introduced a new mechanism: Memory. Memory creates what we refer to as the Algorithmic Self-portrait —a new form of personalization derived from users’ self-disclosed information divulged within private conversations. While memory enables more coherent exch
678anges, the underlying processes of memory creation remain opaque, raising critical questions about data sensitivity, user agency, and the fidelity of the resulting portrait.To bridge this research gap, we analyze 2,050 memory entries from 80 real-world ChatGPT users. Our analyses reveal three key findings: (1) a striking 96% of memories in our dataset are created unilaterally by the conversational system, potentially shifting agency away from the user; (2) Memories, in our dataset, contain a rich mix of GDPR-defined personal data (in 28% memories) along with psychological insights about participants (in 52% memories); and (3) A significant majority of the memories (84%) are directly grounded in user context, indicating faithful representation of the conversations. Finally, we introduce a framework— Attribution Shield —that anticipates these inferences, alerts about potentially sensitive memory inferences, and suggests query reformulations to protect personal information without sacrificing utility.</p>
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706    <div class="title">Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs)</div>
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711      
712      Anna
713            Neumann, Elisabeth
714            Kirsten, Muhammad Bilal
715            Zafar, and
716        <span class="more-authors" title="click to view 1 more author" onclick="
717              var element = $(this);
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743    <div class="periodical">
744      <em>In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency</em>,  Jul 2025
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793        <p>System prompts in Large Language Models (LLMs) are predefined directives that guide model behaviour, taking precedence over user inputs in text processing and generation. LLM deployers increasingly use them to ensure consistent responses across contexts. While model providers set a foundation of system prompts, deployers and third-party developers can append additional prompts without visibility into others’ additions, while this layered implementation remains entirely hidden from end-users. As system prompts become more complex, they can directly or indirectly introduce unaccounted for side effects. This lack of transparency raises fundamental questions about how the position of information in different directives shapes model outputs. As such, this work examines how the placement of information affects model behaviour. To this end, we compare how models process demographic information in system versus user prompts across six commercially available LLMs and 50 demographic groups. Our analysis reveals significant biases, manifesting in differences in user representation and decision-making scenarios. Since these variations stem from inaccessible and opaque system-level configurations, they risk representational, allocative and potential other biases and downstream harms beyond the user’s ability to detect or correct. Our findings draw attention to these critical issues, which have the potential to perpetuate harms if left unexamined. Further, we argue that system prompt analysis must be incorporated into AI auditing processes, particularly as customisable system prompts become increasingly prevalent in commercial AI deployments.</p>
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820    <!-- Title -->
821    <div class="title">The Impact of Inference Acceleration on Bias of LLMs</div>
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824      
825
826      
827      Elisabeth
828            Kirsten, Ivan
829            Habernal, Vedant
830            Nanda, and
831        <span class="more-authors" title="click to view 1 more author" onclick="
832              var element = $(this);
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859      <em>In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)</em>,  Apr 2025
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910        <p>Last few years have seen unprecedented advances in capabilities of Large Language Models (LLMs). These advancements promise to benefit a vast array of application domains. However, due to their immense size, performing inference with LLMs is both costly and slow. Consequently, a plethora of recent work has proposed strategies to enhance inference efficiency, e.g., quantization, pruning, and caching. These acceleration strategies reduce the inference cost and latency, often by several factors, while maintaining much of the predictive performance measured via common benchmarks. In this work, we explore another critical aspect of LLM performance: demographic bias in model generations due to inference acceleration optimizations. Using a wide range of metrics, we probe bias in model outputs from a number of angles. Analysis of outputs before and after inference acceleration shows significant change in bias. Worryingly, these bias effects are complex and unpredictable. A combination of an acceleration strategy and bias type may show little bias change in one model but may lead to a large effect in another. Our results highlight a need for in-depth and case-by-case evaluation of model bias after it has been modified to accelerate inference.This paper contains prompts and outputs which may be deemed offensive.</p>
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